46

A Project Report

On

FINGERPRINT RECOGNITION

Submitted to JNTU in partial fulfillment of the requirement for the award of Bachelor of TechnologyIn

Electronics and Communication Engineering

ByHEENA TARANUM

07361A0430

SWATHI 07361A04A2

CHAITANYA KUMARI 07361A0416

Under the valuable guidance of

Prof. SANDEEP V.M.

Department ofELECTRONICS AND COMMUNICATION ENGINEERING

JAYA PRAKASH NARAYAN COLLEGE OF ENGINEERING

DHARMAPUR, MAHABUBNAGAR-509001(A.P.)

AFFILIATED TO JNTU, ACCREDITED BY N.B.A2010-2011DEPARTMENT OF ELECTRONICS & COMMUNICATION ENGINEERING

JAYAPRAKASH NARAYAN COLLEGE OF ENGINEERING

DHARMAPUR, MAHABOOBNAGAR 509381 (A.P.)

Affiliated to J.N.T.U., Accredited by N.B.A

CERTIFICATE This is to certify that the Project on FINGERPRINT RECOGNITION is a bonafide work done by Mr. /Ms. HEENA TARANUM bearing Roll. No. 07361A0430 in partial fulfillment of the requirement of the award for the degree of Bachelor of Technology in Electronics and Communications Engineering J.N.T.U., Hyderabad during the year 2010-2011.Guide and H.O.D.

Sri. Prof.Sandeep V.M. External Examiner:Professor& Head,

Dept of E.C.E.INDEXINDEX1. Abstract2. Introduction3. Background3.1. What is fingerprint?3.2. Classifying fingerprints3.3. History of fingerprint recognition3.3.1. William west3.3.2. Minutia-based algorithm3.3.3. Spectrum analysis3.3.4. Hybrid technology4. Fingerprint recognition4.1. Preprocessing4.1.1. Image enhancement4.1.2. Noise reduction

4.1.3. Binarization

4.1.4. Thinning

4.2. Segmentation

4.3. Feature extraction

4.3.1. Global extraction

4.3.1.1. Mean and STD of binary image

4.3.1.2. Mean and STD of gray image

4.3.2. Local extraction

4.3.2.1. Level 1

4.3.2.2. Level 2

4.3.3. Data base4.3.3.1. Average of images, then finding feature

4.3.3.2. Finding feature, then averaging values

5. Classification of scattering

6. Conclusion and future scope

7. References

ABSTRACT

1. ABSTRACTA fingerprint is an imprint made by the pattern of ridges on the pad of a human finger, in our process; segmentation is takes place, is the goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.

For feature extraction, We are using global method and local method. Many object recognition systems use global features that describe an entire image. Global method can be done in two ways. One way is for binary image, another is for gray image.

Local feature is an image pattern which differs from its immediate neighborhood. It is usually associated with a change of image property or several properties simultaneously. The image properties considered as feature. Local method can be done in two ways. One way is level 1, another is level 2.

INTRODUCTION

2. INTRODUCTION

Identity verification in computer systems is done based on measures like keys, cards, passwords, PIN and so forth. Unfortunately, these may often be forgotten, disclosed or changed. A reliable and accurate identification/verification technique may be designed using biometric technologies, which are further based on the special characteristics of the person such as face, iris, fingerprint, signature and so forth. This technique of identification is preferred over traditional passwords and PIN-based techniques for various reasons:

The person to be identified is required to be physically present at the time of identification.

Identification based on biometric techniques obviates the need to remember a password or carry a token.

A biometric system essentially is a pattern recognition system that makes a personal identification by determining the authenticity of a specific physiological or behavioral characteristic possessed by the user. Biometric technologies are thus defined as the automated methods of identifying or authenticating the identity of a living person based on a physiological or behavioral characteristic. A biometric system can be either an identification system or a verification (authentication) system; both are defined below.

Identification: One to Many A comparison of an individuals submitted biometric sample against the entire database of biometric reference templates to determine whether it matches any of the templates.

Verification: One to One A comparison of two sets of biometrics to determine if they are from the same individual.

Biometric authentication requires comparing a registered or enrolled biometric sample (biometric template or identifier) against a newly captured biometric sample (for example, the one captured during a login). This is a three-step process (Capture, Process, Enroll) followed by a Verification or Identification.During Capture, raw biometric is captured by a sensing device, such as a fingerprint scanner or video camera; then, distinguishing characteristics are extracted from the raw biometric sample and converted into a processed biometric identifier record (biometric template). Next is enrollment, in which the processed sample (a mathematical representation of the template) is stored/ registered in a storage medium for comparison during authentication. In many commercial applications, only the processed biometric sample is stored. The original biometric sample cannot be reconstructed from this identifier.Among the various biometric technologies being considered are fingerprint, facial features, hand geometry, voice, iris, retina, vein patterns, palm print, DNA, keystroke dynamics, ear shape, odor, signature and so forth.

2.1 Fingerprint

Fingerprint biometric is an automated digital version of the old ink-and-paper method used for more than a century for identification, primarily by law enforcement agencies (Maltoni, 2003). The biometric device requires each user to place a finger on a plate for the print to be read. Fingerprint biometrics currently has three main application areas: large-scale Automated Finger Imaging Systems (AFIS), generally used for law enforcement purposes; fraud prevention in entitlement programs; and physical and computer access. A major advantage of finger imaging is the long-time use of fingerprints and its wide acceptance by the public and law enforcement communities as a reliable means of human recognition. Others include the need for physical contact with the optical scanner, possibility of poor-quality images due to residue on the finger such as dirt and body oils (which can build up on the glass plate), as well as eroded fingerprints from scrapes, years of heavy labor or mutilation.

2.2 fingerprint recognitionIn our method firstly we reducing the noise and then segmentation is done.

In our method we are using for feature extraction mainly two methods, they are global method and local method. In global method we are finding mean and varience of binary image and original image.By using binary image (in this we converting original image into binary and proceeding next steps), finding the images means and averaging all values to get a single value. This single value is used for matching by comparing that value to each of the mean value of image. But this method is providing better result so going for another method.

In another method instead of binary image we using original image, and calculating same process as mentioned above. And matching is done by using that average value and individual mean value of image value .in this process we find the better result than previous one.In local method, Local feature is an image pattern which differs from its immediate neighborhood. In this method we are dividing into some equal windows in 3*3 or 5*5 or 7*7 window. This entire process is done into two types; they are level one and level2.In level 1, only taking the mean and standard deviation of the image with respect to that window. And taking the average of that individual values and comparison is done between average value and individual value.

In level2 level1 process and mean of mean ,mean of std , std of mean and std of std is calculating for individual images and taking average of that images (m of m, m of s, s of m and s of s are averaging separately).at last we comparing with all image individual values to average value.BACK GROUND3. BACK GROUND3.1 WHAT IS FINGER PRINT?

3.1.1 Fingerprint:

1. The characteristic dermal ridges on the finger. This is the original meaning of fingerprint. 2. The characteristic pattern of the peptide fragments of a protein that have been subjected electrophoresis and, at a right angle, chromatography. Peptide fingerprinting was invented by Vernon Ingram in 1957.

3. The characteristic pattern of DNA fragments identified by Southern hybridization or by PCR (polymerase chain reaction) DNA fingerprinting was invented by Alec Jeffreys in 1984. A fingerprint is an imprint made by the pattern of ridges on the pad of a human fingerSometimes the prints are invisible, in which case they are called latent fingerprints, but there are chemical techniques such as cyanoacrylate fuming and ninhydrin spray that can make them visible.

Recently the American Federal Bureau of Investigation adopted a wavelet-based system for efficient storage of fingerprint data, developed by Ingrid Daubechies. In the 2000s, electronic fingerprint readers have been introduced for security applications such as identification of computer users (log-in authentication). However, early devices have been discovered to be vulnerable to quite simple methods of deception, such as fake fingerprints cast in gels.

There is some controversy over the uniqueness of fingerprints. Even those who accept their uniqueness sometimes argue that the techniques used to compare fingerprints are fallible. The same fingerprint as it would be detected on a surface.

Fingerprint analysis (or Dactylographic, a term mainly used in the US) is the science of using fingerprints to uniquely identify someone. Humans leave behind prints of the ridges of the skin on their fingertips when handling certain materials. The pattern of ridges is thought to be unique for each person and in practice has proved unique enough to identify the person who left the fingerprint. 3.2 Classifying fingerprints:The Galton-Henry system of fingerprint classification, published in June 1900, was A fingerprint is the impression made by the papillary ridges on the ends of the fingers and thumbs. Fingerprints afford an infallible means of personal identification, because the ridge arrangement on every finger of every human being is unique and does not alter with growth or age. Fingerprints serve to reveal an individual's true identity despite personal denial, assumed names, or changes in personal appearance resulting from age, disease, plastic surgery, or accident. The practice of utilizing fingerprints as a means of identification, referred to as dactyloscopy, is an indispensable aid to modern law enforcement.

Each ridge of the epidermis (outer skin) is dotted with sweat pores for its entire length and is anchored to the dermis (inner skin) by a double row of peg like protuberances, or papillae. Injuries such as superficial burns, abrasions, or cuts do not affect the ridge structure or alter the dermal papillae, and the original pattern is duplicated in any new skin that grows. An injury that destroys the dermal papillae, however, will permanently obliterate the ridges.

Any ridged area of the hand or foot may be used as identification. However, finger impressions are preferred to those from other parts of the body because they can be taken with a minimum of time and effort, and the ridges in such impressions form patterns (distinctive outlines or shapes) that can be readily sorted into groups for ease in filing.

Early anatomists described the ridges of the fingers, but interest in modern fingerprint identification dates from 1880, when the British scientific journal Nature published letters by the Englishmen Henry Faulds and William James Herschel describing the uniqueness and permanence of fingerprints.

Their observations were experimentally verified by the English scientist Sir Francis Galton, who suggested the first elementary system for classifying fingerprints based on grouping the patterns into arches, loops, and whorls. Galton's system served as the basis for the fingerprint classification systems developed by Sir Edward R. Henry, who later became chief commissioner of the London metropolitan police, and by Juan Vucetich of Argentina.

Officially introduced at Scotland Yard in 1901 and quickly became the basis for its criminal-identification records. The system was adopted immediately by law-enforcement agencies in the English-speaking countries of the world and is now the most widely used method of fingerprint classification. Juan Vucetich, an employee of the police of the province of Buenos Aires in 1888, devised an original system of fingerprint classification published in book form under the title Dactiloscopa Compared (1904; "Comparative Fingerprinting"). His system is still used in most Spanish-speaking countries.

Fingerprints are classified in a three-way process: by the shapes and contours of individual patterns, by noting the finger positions of the pattern types, and by relative size, determined by counting the ridges in loops and by tracing the ridges in whorls. The information obtained in this way is incorporated in a concise formula, which is known as the individual's fingerprint classification.

There are several variants of the Henry system, but that used by the Federal Bureau of Investigation (FBI) in the United States recognizes eight different types of patterns: radial loop, ulnar loop, double loop, central pocket loop, plain arch, tented arch, plain whorl, and accidental. Whorls are usually circular or spiral in shape. Arches have a mound like contour, while tented arches have a spike like or steeple like appearance in the center. Loops have concentric hairpin or staple-shaped ridges and are described as "radial" or "ulnar" to denote their slopes; ulnar loops slope toward the little finger side of the hand, radial loops toward the thumb. Loops constitute about 65 percent of the total fingerprint patterns; whorls make up about 30 percent and arches and tented arches together account for the other 5 percent. The most common pattern is the ulnar loop.

Dactyloscopy, the technique of fingerprinting, involves cleaning the fingers in benzene or ether, drying them, and then rolling the balls of each over a glass surface coated with printer's ink. Each finger is then carefully rolled on prepared cards according to an exact technique designed to obtain a light gray impression with clear spaces showing between each ridge so that the ridges may be counted and traced. Simultaneous impressions are also taken of all fingers and thumbs.

Latent fingerprinting involves locating, preserving, and identifying impressions left by a culprit in the course of committing a crime. In latent fingerprints, the ridge structure is reproduced not in ink on a record card but on an object in sweat, oily secretions, or other substances naturally present on the culprit's fingers. Most latent prints are colorless and must therefore be "developed," or made visible, before they can be preserved and compared. This is done by brushing them with various gray or black powders containing chalk or lampblack combined with other agents. The latent impressions are preserved as evidence either by photography or by lifting powdered prints on the adhesive surfaces of tape.

Though the technique and its systematic use originated in Great Britain, fingerprinting was developed to great usefulness in the United States, where in 1924 two large fingerprint collections were consolidated to form the nucleus of the present file maintained by the Identification Division of the FBI. The division's file contained the fingerprints of more than 90 million persons by the late 20th century. Fingerprint files and search techniques have been computerized to enable much quicker comparison and identification of particular prints.

Other "fingerprinting" techniques have also been developed. These include the use of a sound spectrograph--a device that depicts graphically such vocal variables as frequency, duration, and intensity--to produce voice graphs, or voiceprints, and the use of a technique known as DNA fingerprinting, an analysis of those regions of DNA that vary among individuals, to identify physical evidence (blood, semen, hair, etc.) as belonging to a suspect. The latter test has been used in paternity testing as well as in forensics.There are three basic fingerprint patterns: Arch, Loop and Whorl. There are more complex classification systems that further break down the pattern to plain arches or tented arches. Loops may be radial or ulnar. Whorls also have smaller classifications. However, the five most commonly used are: whorl, right loop, left loop, arch and tented arch.

There are three main fingerprint patterns: arches, loops and whorls.

Arches are found in about 5% of fingerprint patterns encountered. The ridges run from one side to the other of the pattern, making no backward turn. Ordinarily, there is no delta in an arch pattern but where there a delta; no re-curving ridge must intervene between the core and delta points. There are four types of arch patterns: plain arches, radial arches, ulnar arches and tented arches. Plain arches have an even flow of ridges from one side to the other of the pattern; no significant up thrusts and the ridges enter on one side of the impression, and flow out the other with a rise or wave in the center. The ridges of radial arches slope towards the thumb, have one delta and no re-curving ridges. On ulnar arches, the ridges slope towards the little finger, have one delta and no re-curving ridges. Tented arches have an angle, an up thrust, or two of the three basic characteristics of the loop. They dont have the same "easy" flow that plain arches do and particularly have significant up thrusts in the ridges near the middle that arrange themselves on both sides of a spine or axis towards which the adjoining ridges converge and appear to form tents.

Plain Arch Tented Arch

Loops occur in about 60-70 % of fingerprint patterns encountered. One or more of the ridges enters on either side of the impression, re-curves, touches or crosses the line running from the delta to the core and terminates on or in the direction of the side where the ridge or ridges entered. Each loop pattern has is one delta and one core and has a ridge count. Radial loops are named after the radius, a bone in the forearm that joins the hand on the same side as the thumb. The flow of the pattern in radial loops runs in the direction of the radius (toward the thumb). Radial loops are not very common and most of the time radial loops will be found on the index fingers. Ulnar loops are named after the ulna, a bone in the forearm. The ulna is on the same side as the little finger and the flow of the pattern in a ulnar loop runs in the direction of the ulna (toward the little finger).

Radial Loop Ulnar Loop

Whorls are seen in about 25-35 % of fingerprint patterns encountered. In a whorl, some of the ridges make a turn through at least one circuit. Any fingerprint pattern which contains 2 or more deltas will be a whorl pattern. There are four types of whorl patterns. Plain whorls consist of one or more ridges which make or tend to make a complete circuit with two deltas, between which an imaginary line is drawn and at least one re-curving ridge within the inner pattern area is cut or touched. Central pocket loop whorls consist of at least one re-curving ridge or an obstruction at right angles to the line of flow, with two deltas, between which when an imaginary line is drawn, no re-curving ridge within the pattern area is cut or touched. Central pocket loop whorl ridges make one complete circuit which may be spiral, oval, circular or any variant of a circle. Double loop whorls consist of two separate and distinct loop formations with two separate and distinct shoulders for each core, two deltas and one or more ridges which make, a complete circuit. Between the two at least one re-curving ridge within the inner pattern area is cut or touched when an imaginary line is drawn. Accidental whorls consist of two different types of patterns with the exception of the plain arch; have two or more deltas or a pattern which possess some of the requirements for two or more different types or a pattern which conforms to none of the definitions.

Plain Whorl Central Pocket Whorl

Double Loop Whorl Accidental Whorl

3.3 History of Fingerprint Recognition:3.3.1 William West:The most famous case in the history of fingerprinting occurred in the late 19th century a man was spotted in the incoming prisoner line at the U.S. Penitentiary in Leavenworth, Kansas by a guard who 'knew' him and had just seen him already in the prison population. Upon examination, the incoming prisoner claimed to be named Will West, while the (not escaped) existing prisoner was named William West. According to their Bertillon measurements they were essentially indistinguishable. As they were not twins, the Bertillon system came into some question. However, their fingerprints were different, and fingerprint identification received a significant boost in credibility.

Fingerprint imaging technology has been in existence for centuries. The use of fingerprints as a unique human identifier dates back to second century B.C. China, where the identity of the sender of an important document could be verified by his fingerprint impression in the wax seal [3].The first modern use of fingerprints occurred in 1856 when Sir William Herschel, the Chief magistrate of the Hooghly district in Jung poor, India, had a local businessman,

Rajyadhar Konia, impress his handprint on the back of a contract. Later, the right index and middle fingers were printed next to the signature on all contracts made with the locals. The purpose was to frighten the signer of repudiating the contract because the locals believed that personal contact with the document made it more binding. As his fingerprint collection grew Sir Herschel began to realize that fingerprints could prove or disprove identity.

The 19th century introduced systematic approaches to matching fingerprints to certain individuals. One systematic approach, the Henry Classification System, based on patterns such as loops and whorls, is still used today to organize fingerprint card files.

In the late 1960s NEC worked with the FBI and the Home Office in London, which had been working on a system for New Scotland Yard from the late 1960s, to eventually develop a minutia-based fingerprint identification system. It was initially installed in Tokyo in 1981 and in San Francisco in 1983.

The first country to adopt a national computerized form of fingerprint imaging was Australia in 1986, which implemented fingerprint imaging technology into its law enforcement system.

In 1996 after nearly a year of study, the National Institute of Standards and Technology has been convinced that minutia is an acceptable way to store fingerprint biometric data on smart cards. With the NIST acceptance of minutia it became inevitable this would set an industry standard.

3.3.2 Minutia-Based Algorithm

Minutia-based algorithms extract information such as ridge ending, bifurcation, and short ridge from a fingerprint image.

Short RidgeRidge EndingBifurcation

(Image source Wikipedia)

These features are then stored as mathematical templates. The identification or verification process compares the template of the live image with a database of enrolled templates (identification), or with a single enrolled template (authentication).

(Image source National Institute of Standards and Technology)

People with no or few minutia points (special skin conditions) cannot enroll or use the system effectively. This is exemplified by the fingerprint immigration programs where finger moistening peripherals are standard. Moreover, a low number of minutia points can be a limiting factor for security of the algorithm. This can lead to false minutia points (areas of obfuscation that appear due to low-quality enrollment, imaging, or fingerprint ridge detail). In an application environment, enrollment without assistance may take several attempts due to poor position or lack of pressure. While not quantified, user frustration will certainly have a negative impact on technology acceptance.

3.3.3 Spectrum Analysis

Utilizing research from Nagoya Institute of Technology Graduate School in Japan, DDS has developed an algorithm based on Spectrum Analysis. This technique captures cross sections of a sliced fingerprint pattern and converts them to waves. Spectrum analysis uses the spectral series of the waves as feature information, finding the maximum correlations in the wave and verifies the identity of the fingerprint.

This algorithm of spectrum analysis works extremely well because this algorithm extracts characteristics from the concavo-convex information of a fingerprint without being influenced by the position of the characteristic points used in the conventional Minutia and Pattern-matching method.

In the course of verification under the spectrum analysis algorithm, it is not necessary to store the fingerprint image itself in the system which eliminates the possibility of exposure or leakage of fingerprint images. In principle, it is impossible to regenerate original fingerprint image from the extracted characteristics of images. This addresses issues raised by the IEEE on fingerprint reconstruction of minutiae based systems.

The algorithm performs extremely well in controlled environments where positioning of the finger in enrollment and verification are similar. However, with disparate fingerprint positioning for enrollment and verifications results can be less than desired. This requirement limits the application developer to more controlled ergonomic environments and may reduce some commercial viability.

3.3.4 The Hybrid technology:To accentuate the strengths of both the Spectrum Analysis and Minutiae-based algorithm and limited the inherent weaknesses, the company has combined both algorithms and created a Hybrid algorithm. The combined technology provides for rapid and accurate enrollment and verification in difficult environments.

This Hybrid algorithm extends beyond combining scores from both techniques to form a single decision; instead the technology utilizes a proprietary technique of Shading. Shading analyzes the scores of both results and places and associates an importance value with each. Using an algorithm based upon a database of past results, a final score is calculated by using the individual results as a function of importance. With shading, if one score is high and one is low, more decision weighting is placed upon the higher score. If both scores are low then information from both are weighed more equally and the results are combined together for a final decision. This ability allows for both algorithms to have low scores and still be accurate.

The technology addresses both the strengths of both as well as the challenges. Spectrum analysis can work well with poor image quality and difficult to read fingerprints and minutiae-based algorithms can work very well with angles.

Hybrid Enrollment Example

FINGERPRINT RECOGNITION

4. FINGER PRINT RECOGNITION4.1 PRE PROCESSING

This is an essential part of finger print recognition, in this step the image is made ready for the actual matching, the input of this phase is original fingerprint image and the final output of this step is minutiae of that image. Our proposed algorithm is as follows.

4.1.1 Image enhancement:

The aim of image enhancement is to improve the interpretability or perception of information in images for human viewers, or to provide `better' input for other automated image processing techniques. Image enhancement techniques can be divided into two broad categories:

1. Spatial domain methods, which operate directly on pixels, and

2. Frequency domain methods, which operate on the Fourier transform of an image. Three types of degradations affect on quality of the fingerprint image, the ridges gets some gaps: parallel ridges connected due to noise and natural effect to the finger like cuts, wrinkles and injuries. The finger print enhancement is anticipated to improve the contrast between ridges and valleys and reduce noises in the fingerprint images.High quality finger print image is very important for fingerprint verification or identification to work properly. In real life, the quality of the fingerprint image is affected by noise like smudgy area created by over-inked area, breaks in ridges created by under-inked area, changing the positional characteristics of fingerprint features due to skin resilient in nature, dry skin leads to fragmented and low contrast ridges, wounds may cause ridge discontinuities and sweat on fingerprints also leads to smudge marks and connects parallel ridges.Following figures shows A. original finger print image and B. enhanced image.

4.1.2 Noise reduction

Noise is an unwanted perturbation to a wanted signal. Image noise is generally regarded as an undesirable by-product of image capture. Noise reduction is the process of removing noise from a picture (here it is the fingerprint image).We have checked and used different types of filtering methods like median filter, global and adaptive thresholding to reduce the noise.4.1.3 BinarizationIn the pre-processing stage, the image is converted from grayscale to black and white. This is done by calculating the average background intensity and subtracting this value from the grayscale image. Next grey scale threshold (basic global and adaptive thresholding) is calculated so pixels above this value become black, and the ones below become white.

Figure: (a) Original Fingerprint (b) Binarized Fingerprint.4.1.4 Thinning

Next the ridges must be thinned to a width of one-pixel. In this step two consecutive fast parallel thinning algorithms are applied, in order to reduce to a single pixel the width of the ridges in the binary image. These operations are necessary to simplify the subsequent structural analysis of the image for the extraction of the fingerprint minutiae. The thinning must be performed without modifying the original ridge structure of the image. During this process, the algorithms cannot miscalculate beginnings, endings and or bifurcation of the ridges, neither ridge can be broken.

Figure shows the thinned image of the binarized image 4.2 SEGMENTATION:In computer vision, segmentation refers to the process of partitioning a digital image into multiple segments (sets of pixels, also known as super pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics.

The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image. Each of the pixels in a region is similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s).

In this particular method, we first reading the particular image, and figure is shown by appropriate command of mat lab. Then we moves for convolution.

Convolution computes the two-dimensional convolution of matrices A and B. If one of these matrices describes a two-dimensional finite impulse response (FIR) filter, the other matrix is filtered in two dimensions. The size of C (convolution) in each dimension is equal to the sum of the corresponding dimensions of the input matrices, minus one. That is, if the size of A is [ma, na] and the size of B is [mb, nb], then the size of C is [ma+mb-1, na+nb-1].

Convolution for two dimensional matrices is given below.

C = conv2 (A, B)

In our finger print reorganization convolution is takes place between main image and ones matrix (in this all rows and columns all denoted by 1) image. The resultant image is shown by using figure.

In below shown figure, first image is original image and second image is segmented image. In second image is portioned as black and white, i.e. the image is represented in white color.

4.3 Feature extraction In pattern recognition and in image processing, feature extraction is a special form of dimensionality reduction. This is a part of image processing, where a part of the whole image is taken to recognize its specific pattern and then it is measured to classify the image on the basis of that particular measurement.

Normally the second step in image analysis that seeks to measure the individual features of the blobs or objects in the scene. Image features at various levels of complexity are extracted from the image data. Typical examples of such features are Lines, edges and ridges. Localized interest points such as corners, blobs or points.

4.3.1 Global extraction

Many object recognition systems use global features that describe an entire image. Most shape and texture descriptive fall into this category; such features are attractive because they produce very compact representations of images, where each image corresponds to a point in a high dimensional feature space. Global features are sensitive to clutter and occlusion. 4.3.1.1 Mean and standard deviation of binary image: In this algorithm we used very easy methods for better understanding; we commonly use mean and standard deviation, which is familiar and implementing easily.In firstly images and portioning into eight groups by adding noise, and forming as individual images. Now the image reading is done, and it is converted into gray to binary. Gray is a color of combination of black to white which has 0 to 255 pixels. Binarization is a process of converting only in two colors; they are black and white, 1 for white and 0 for black.

Figure: (a) Original Fingerprint (b) Binarized Fingerprint. Applying the mean (average) and standard deviation of each image by different ways i.e. by normal, increasing size, decreasing size, horizontal rotation, vertical rotation and rotation in particular angle. Mean values are shown in table belowImage Normal Zoom Horizontal Vertical Rotation

10.92070.92070.92070.92070.9207

20.95760.95760.95760.95760.9576

30.99970.99970.99970.99970.9997

40.87330.87330.87330.87330.8733

50.97000.97000.97000.97000.9700

60.95150.95150.95150.95150.9515

70.92450.92450.92450.92450.9245

80.99500.99500.99500.99500.9950

For standard deviation values are shown in table below.

Image Normal Zoom Horizontal Vertical Rotation

10.17220.17220.17220.17220.1722

20.16650.16650.16650.16650.1665

30.22410.22410.22410.22410.2241

40.01320.01320.01320.01320.0132

50.13480.13480.13480.13480.1348

60.07940.07940.07940.07940.0794

70.00990.00990.00990.00990.0099

80.00660.00660.00660.00660.0066

For matching taking the average of every group values and checking each value in it. This method is not sufficient for feature extraction of matching fingerprint; binary image contains only 50% of white and 50% of black. The mean of that is may be 50 it and STD is relatively very small, if some images having poor binarization so that will get insufficient values. Due to this reason we are going to use direct image in feature extraction.4.3.1.2 Mean and standard deviation of gray image In this method, mean and standard deviation is calculated for gray images of respected groups. Gray image is a combination of black to white colors ranging from 0 to 255.0 for black and up to 255 is different combination of colors. (All values of each group are averaging them for an individual value to represent a group value).The details of mean values are shown in below table.

ImageNormalZoomHorizontalVerticalRotation

1178.1069178.1069178.1069178.1069178.1069

2177.3688177.3688177.3688177.3688177.3688

3177.4977177.4977177.4977177.4977177.4977

4198.3028198.3028198.3028198.3028198.3028

5187.2216187.2216187.2216187.2216187.2216

6197.4129197.4129197.4129197.4129197.4129

7214.1794214.1794214.1794214.1794214.1794

8226.2012226.2012226.2012226.2012226.2012

The details of standard deviation are as shown in below table.

ImageNormalZoomHorizontalVerticalRotation

133.145233.145233.145233.145233.1452

233.131333.131333.131333.131333.1313

333.858333.858333.858333.858333.8583

430.566530.566530.566530.566530.5665

524.490424.490424.490424.490424.4904

626.503926.503926.503926.503926.5039

719.542019.542019.542019.542019.5420

821.667921.667921.667921.667921.6679

After finding the values of each group result, matching is performed by comparing those values to individual images.

As compared to above method it gives the better result.4.3.2 Local extraction Local feature is an image pattern which differs from its immediate neighborhood. It is usually associated with a change of image property or several properties simultaneously. The image properties considered are intensity color and texture. In this process we are calculating by dividing whole image into some equal parts which is referred as window. Window size will be 3, 5, 7 ..etc.It is again divided into 2 types; they are level 1 and level 2.

4.3.2.1 Level 1

In this type of windowing sequences calculating only mean and STD is directly. Finding the mean and variance is gives the value of that particular part of the image. So using it is not effective of finding feature.In this method, we compute the mean and variance for particular window size.4.3.2.2 Level 2

In this type of widowing sequence along with the mean and STD, again calculating mean of mean, mean of STD ,STD of mean , STD of STD. it is a level two process.

In this section we are using level 2 for better results. While performing mean of mean stands for mean of the total image which is portioned into some small images, and to obtain a single value. STD a measure of the variation between individuals on a variables, the variance is used as a measure of how far a set of numbers are spread out from each other. It is one of several descriptors of a probability distribution, describing how far the numbers lie from the mean (expected value). In particular, the variance is one of the moments of a distribution. In that context, it forms part of a systematic approach to distinguishing between probability distributions. While other such approaches have been developed, those based on moments are advantageous in terms of mathematical and computational simplicity.4.3.3 Data base

It is of two types, they are mentioned as follows.

4.3.3.1. Average of images and finding features: Averaging images and finding the values of that image. But this process using in finger print is not sufficient, because in fingerprints orientations are different. It is mainly used in iris recognition and face recognition which has less orientation (in this recognition systems first averaging images and then finding its feature values).

4.3.3.2. Finding feature and then averaging values: In this method first finding feature and then averaging values is nothing but first finding the values of mean and variance for individual values and finding average of those values.

In this process we are using level 2 form for better results, first finding mean and variance for particular window size ,and then again finding the mean of mean ,mean of STD,STD of mean and STD of STD.For level 1:

In this method mean &standard deviation of a fingerprint image are calculated for every individual image of the group and then average the mean &standard deviation of each person to form a one value for every group representation. Arrange them in matrix form. The matrix is as follows. Personaverage of meanaverage of standard deviation

101142.592760.3239

102135.661670.4338

103140.892466.7694

104150.328863.0280

105179.788734.4733

106134.582062.1796

107112.766065.2008

108155.199970.8154

The matrix loaded into the mat lab.For image matching take the fingerprint image of mean & standard deviation are calculated. Check the values in the above matrix. If that value is nearly equals to the any of the row of a matrix. Then that fingerprint image is matching to that particular group.This method is not suited for the detection of fingerprint. Because the possibilities of matching is low so, level2 method is preferred.

For level2: In this method finger print image is divided into 5*5 windows or 3*3 windows or 7*7 windows. Find the mean and standard deviation of the window. The values of mean is stored in new image the values of standard deviation is stored in another new image. Find the mean &standard deviation of mean image. Find the mean &standard deviation of standard deviation image. So, we get 4 values.

The 4 values are mean of mean, mean of standard deviation, standard deviation of mean, standard deviation of standard deviation. The values are arranged into the form of matrix.

The matrix for 3*3 windows PersonValue1Value2Value3Value4

101182.74768.541733.54284.7375

102180.22358.695230.66295.1416

103194.89667.822429.52374.7500

104205.67268.225527.18734.7734

105213.14286.517526.22424.5586

106213.14286.517526.22424.5586

107217.34415.481524.18453.8095

108185.99576.681232.18244.3552

The matrix for 5*5 windowsPersonValue1Value2Value3Value4

101181.073312.729034.90376.2025

102178.650412.545032.30016.5500

103193.251711.538031.99816.2999

104204.044212.022130.35966.1085

105181.208410.363536.52205.5687

106211.48209.242030.43486.0274

107215.68817.929229.14695.0887

108184.35219.513734.55255.8304

The matrix for 7*7 windows

PersonValue1Value2Value3Value4

101179.406333.54288.54174.7375

102180.223530.66298.69525.1416

103194.896629.52378.2244.7500

104205.672627.18738.22554.7734

105182.956334.65457.19934.3590

106213.142826.22426.51754.5586

107217.344124.18455.48153.8095

108185.995732.18246.68124.3552

The above matrices are loaded into the mat lab for processing these matrices are saved in individual way. And for matching process those values are loaded into mat lab and compared with any finger print image .in this process we find the minimum distance to that entire matrix and image values. In this process, if the value of image is near to any row we conclude that it is from that group.Here we are using different window sizes for every window size the probability of matching is different. It is shown in below table.Window sizesProbability of matching correctProbability of matching wrong

360%40%

570%30%

780%20%

From the above table, window 7*7 is obtaining correct is greater than 3*3 and 5*5

5. CLASSIFICATION OF SCATTERING5. CLASSIFICATION OF SCATTERING CLUSTERING TECHNIQUE:

Cluster analysis, also called segmentation analysis or taxonomy analysis, creates groups, or clusters, of data. Clusters are formed in such a way that objects in the same cluster are very similar and objects indifferent clusters are very distinct. Measures of similarity depend on the application. 'Cluster analysis' is a class of statistical techniques that can be applied to data that exhibit natural groupings. Cluster analysis sorts through the raw data and groups them into clusters. A cluster is a group of relatively homogeneous cases or observations. Objects in a cluster are similar to each other. They are also dissimilar to objects outside the cluster, particularly objects in other clusters.

The diagram below illustrates the results of a survey that studied drinkers perceptions of spirits (alcohol). Each point represents the results from one respondent. The research indicates there are four clusters in this market.

This method is described as follows.

Firstly image is read by using its appropriate command and it is converted into binary image.1. Formulate the problem - select the variables to which you wish to apply the clustering technique

2. Select a distance measure- various ways of computing distance:

Squared Euclidean distance - the square root of the sum of the squared differences in value for each variable

Manhattan distance - the sum of the absolute differences in value for any variable

Chebyshev distance - the maximum absolute difference in values for any variable

Mahalanobis (or correlation) distance - this measure uses the correlation coefficients between the observations and uses that as a measure to cluster them. This is an important measure since it is unit invariant (can figuratively compare apples to oranges)

3. Select a clustering procedure (see below)

4. Decide on the number of clusters

5. Map and interpret clusters - draw conclusions - illustrative techniques like perceptual maps, icicle plots, and dendrograms are useful

6. Assess reliability and validity - various methods:

repeat analysis but use different distance measure

repeat analysis but use different clustering technique

split the data randomly into two halves and analyze each part separately

repeat analysis several times, deleting one variable each time

repeat analysis several times, using a different order each time

In our method we are finding distance from selected image to cluster by using distance transform of binary image.

Syntax,D = bwdist (BW)

[D, L] = bwdist (BW)

[D, L] = bwdist (BW, method)

Methods include is as shown below.

1. Chessboard: In 2-D, the chessboard distance between (x1, y1) and (x2,y2) is Ch=max ([x1-x2], [y1-y2])

2. 'City block: In 2-D, the city block distance between (x1, y1) and (x2, y2) is Ct=[x1-x2] + [y1-y2]

3.Euclidean': In 2-D, the Euclidean distance between (x1, y1) and (x2, y2) is

Ec=sqrt ((x1-x2) ^2+ (y1-y2) ^2)This is the default method.

4.Quasi-euclidean': In 2-D, the quasi-Euclidean distance between (x1,y1) and (x2,y2) is

Qec=[x1-x2] + (sqrt (2)-1) [y1-y2], [x1-x2]> [y1-y2]

(Sqrt (2)-1)[X1-x2]+ [y1-y2], otherwise

6. CONCLUSION AND FUTURE SCOPE

6. CONCLUSION AND FUTURE SCOPEFingerprint Image enhancement is to make the image clearer for easy further operations. Since the fingerprint images acquired from sensors or other media are not assured with perfect quality, enhancement methods, for increasing the contrast between ridges and furrows and for connecting the false broken points of ridges due to insufficient amount of ink, are very useful to keep a higher accuracy to fingerprint recognition. Two methods are adopted in the work: the first one is Histogram Equalization; the second one is Fourier Transform.

1. Region of interest tells us which region of the fingerprint is very useful for various features matching.

2. Improved thinning in the present work contributes to: The Image becomes perfectly thinned to single pixel width. More number of bifurcations can be detected.

3. Also a program coding with MATLAB going through all the stages of the fingerprint recognition is built. It is helpful to understand the procedures of fingerprint recognition. And demonstrate the key issues of fingerprint recognition.

Overall, a set of reliable techniques have implemented for fingerprint recognition. These techniques can then be used to facilitate the further study of the statistics of fingerprints. The future scope of the work is to do Binarization of the image, to find the direction of the bifurcation and ridges of the image, to extract features, to find actual minutiae .So that the fingerprint recognition could be made better which can improve the future stages and the final outcome. 7. REFFERENCES

7. REFFERENCES

1. Jain, L.C. et al. (Eds.). 1999. Intelligent Biometric Techniques in Fingerprint and Face Recognition. Boca Raton, FL: CRC Press.

2. Lunenburg, Glenn (January 24, 2005). "Are one's fingerprints similar to those of his or her parents in any discernable way?". Scientific American. Retrieved 28 August 2010.3. Thornton, John (May 9, 2000). "Latent Fingerprints, Setting Standards In The Comparison and Identification". 84th Annual Training Conference of the California State Division of IAI.. Retrieved 30 August 2010.4. Diaz, Raul (2007). "Biometrics: Security Vs Convenience". Security World Magazine.. Retrieved 30 August 2010.5. Meghdadi, Majid; Jalilzadeh, Saeed (29 October 2005). "Validity and Acceptability of Results in Fingerprint Scanners". Proceedings of the 7th WSEAS International Conference on Mathematical Methods and Computational Techniques In Electrical Engineering. World Scientific and Engineering Academy and Society.. Retrieved 4 November 2010.6. Setlak, Dale. "Advances in Biometric Fingerprint Technology are Driving Rapid Adoption in Consumer Marketplace". Authentic. Retrieved 4 November 2010.7. Mazumdar, Subhra; Dhulipala, Venkata (2008). "Biometric Security Using Finger Print Recognition"(PDF). University of California, San Diego. p. 3.. Retrieved 30 August 2010.8. Minutia vs. Pattern Based Fingerprint Templates. (2003). Retrieved December 13, 2005, from (archived from www.ibia.org on 2007-09-29)

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